Introduction

This is a project undertaken as part of the Data Science for the 21st Century (DS421) National Science Foundation Research Traineeship. Specifically, it is a “Research Experience for Trainees,” a required part of the DS421 program where the trainee contributes to research “outside of their primary discipline” for 50% of a summer. This particular project builds off of work previously performed by the author, Nathaniel Decker, (Decker et al., 2017) and the research sponsor, Christopher Jones, (Jones & Kammen, 2014, 2015).

The income of households has a dramatic impact on the effect of geography on households’ greenhouse gas (GHG) footprints (C. Jones & Kammen, 2014). The actions of local governments have a profound impact on the patterns of income-mixing in the generation of housing (Glaeser, Gyourko, & Saks, 2005; Terner Center for Housing Innovation, n.d.). We know the order-of-magnitude GHG effect of the siting of new housing in California (Decker et al., 2017; C. Jones & Kammen, 2015), but we don’t know the extent to which these impacts vary by the patterns of income mixing. This is an important policy concern because the means of facilitating the development of housing for low- to moderate-income (LMI) families are very different than the means of facilitating the development of market-rate housing.

While the GHG implications of siting LMI housing are extensively debated and have a major role in the funding of LMI housing, the GHG implications of the siting of new market-rate housing have not resulted in major policy changes or new programs. The raison d’etre of the largest state source of affordable housing funding, the Affordable Housing and Sustainable Communities Program, is GHG reduction. The criteria for California awards of the largest federal source of funding for affordable housing construction, the Low Income Housing Tax Credit, have been revised to lower the GHG emissions of these subsidized households. While some state processes to plan for and promote the construction of market rate housing, such as the housing element process, do consider the GHG emissions impact of he siting of new market-rate housing, these measures are largely toothless (Dillon 2017).

The calculation of the GHG reduction potential stratified by the cost of housing can be used to address this pressing policy issue. This calculation should help clarify the GHG emissions payoff of regulatory changes to facilitate market-rate housing generation in infill areas.

Thought Experiment

To estimate how much the income mix of new development matters this analysis takes the form of a thought experiment. Suppose that the next five years of residential development in California follows the geographic patterns of the last five years. Suppose that the income mix of the new residents of the state roughly matches the income mix of the regions of the state they move to. Given these suppositions we consider two scenarios: (i) subsidies for income-resticted housing are vastly increased and all of this housing is constructed in infill areas, but no policies are put in place to facilitate the development of new market-rate units in infill areas or (ii) development of infill market-rate housing is facilitated to the point where all market-rate housing is placed in infill areas and income-restircted housing is constructed in farther-flung locations.

The thought experiment isolates the impact of income mix by geography. It also acknolwedges that, while the affordable housing development system in California creates a system of financing that promotes the GHG-resposible siting of housing, this goal has not yet been actualized in the various systems for market-rate development. While efforts have been made to attempt to push municipalities to adjust their land use regulations to facilitate the GHG-resposible development of new housing supply (particularly the state’s requirements for Regional Housing Needs Allocations (RHNA), Annual Progress Reports (APRs), and housing elements in comprehensive plans), these systems are ineffective.

Results

So actually, let’s just assume that the next five years of construction (2016-2020) will follow the patterns of the last 5. The California Department of Finance projects that the state will add 1660190 new residents in 628545 households.

If the lowest income households are placed in the lowest HH GHG areas: [Table that breaks out total GHGe by income bracket, summed up across the state, with a total row at the bottom]

inc_bin Households: Market Infill (1,000s) Households: Affordable Infill (1,000s) Emissions: Market Infill (1,000s) Emissions: Affordable Infill (1,000s) Per-HH Emissions: Market Infill Per-HH Emissions: Affordable Infill
MOD2HI 303 343 17,285 19,751 57.0 57.5
SLI 110 92 3,844 3,738 34.8 40.4
VLI 90 67 2,752 2,405 30.6 36.1
XLI 137 99 3,125 2,396 22.8 24.1
Total 640 601 27,006 28,290 145.2 158.1

If the highest income households are placed in the lowest HH GHG areas: [Table that breaks out total GHGe by income bracket, summed up across the state, with a total row at the bottom]

Dividing the State into Regions

Our scenarios won’t assume that all low income households are in SF or not. It’ll break the state up into regions and allocate households within regions. DoF releases population projections at the county level so our regions will be groups of counties. We’ll divide the state up using area median income levels set by HUD.

Digital paup.

References

California Department of Finance. (n.d.). Demographic Projections. Retrieved July 1, 2017, from http://www.dof.ca.gov/Forecasting/Demographics/Projections/

Decker, N., Galante, C., Chapple, K., Martin, A., Elkind, E. N., & Hanson, M. (2017). Right Type, Right Place: Assessing the Environmental and Economic Impacts of Infill Residential Development through 2030. Berkeley, CA: Terner Center for Housing Innovation & Center for Law, Energy and the Environment (CLEE). Retrieved from http://next10.org/right-housing

Dillon, L. (2017, June 29). California lawmakers have tried for 50 years to fix the state’s housing crisis. Here’s why they’ve failed. Los Angeles Times. Retrieved from http://www.latimes.com/projects/la-pol-ca-housing-supply/

Glaeser, E. L., Gyourko, J., & Saks, R. (2005). Why Is Manhattan So Expensive? Regulation and the Rise in Housing Prices. The Journal of Law and Economics, 48(2), 331–369. https://doi.org/10.1086/429979

Jones, C. M., & Kammen, D. M. (2014). Spatial Distribution of U.S. Household Carbon Footprints Reveals Suburbanization Undermines Greenhouse Gas Benefits of Urban Population Density. Environmental Science & Technology, 48(2), 895–902. https://doi.org/10.1021/es4034364

Jones, C. M., & Kammen, D. M. (2015). A Consumption-Based Greenhouse Gas Inventory of San Francisco Bay Area Neighborhoods, Cities and Counties: Prioritizing Climate Action for Different Locations. Bay Area Air Quality Management District. Retrieved from http://escholarship.org/uc/item/2sn7m83z

Terner Center for Housing Innovation. (n.d.). Housing Development Dashboard. Retrieved July 1, 2017, from https://ternercenter.berkeley.edu/dashboard